Role Overview

BCG U — BCG's dedicated capability-building and learning arm — is designing and delivering a national AI acceleration programme in partnership with IMDA. The programme helps Singapore enterprises develop a business-linked digital roadmap and implement a working AI solution — taking cohorts of companies through an 18-week journey from opportunity identification to production deployment with IMDA-appointed tech vendors.

The programme combines in-person bootcamps, 1:1 coaching, structured toolkits, and partnerships with major cloud and AI platform providers. It targets mid-sized non-ICT enterprises across manufacturing, logistics, retail/F&B, professional services, and other sectors — companies with real operational pain points and leadership willing to act.

We are seeking a technically deep practitioner to serve as the technical backbone of the programme — owning solution architecture quality, vendor technical governance, and enterprise-level technical coaching. This role sits within the BCG U programme team and works directly with enterprise leaders and appointed tech vendors.

What You’ll Do

1. Programme Design

• Design the programme's core technical artefacts — AI solution architecture templates, data readiness assessments, integration design frameworks, and technical feasibility checklists that enterprises and vendors work through during bootcamps and coaching
• Define the technical engagement model with vendors — how solutions are scoped, how architecture is reviewed, how feasibility is validated, and how technical quality is governed throughout delivery
• Develop reusable reference architectures and solution patterns tailored to common SME use cases (e.g., predictive analytics, process automation, customer intelligence, document processing)

2. Solution Architecture & Technical Coaching

• Co-facilitate the solution design — making technical concepts (architecture, data, integration, risk) accessible to non-technical enterprise leaders while maintaining technical rigour
• Coach enterprises on translating business requirements into implementable technical scope — including data requirements, system integration points, and realistic deployment plans
• Assess technical feasibility of proposed AI solutions against each enterprise's data maturity, existing systems, and operational constraints
• Ensure solution designs are right-sized for SME contexts — pragmatic, deployable with lean IT teams, and built to deliver business outcomes

3. Implementation Oversight

• Oversee AI solution implementation across the cohort — manage the appointed tech vendors, run governance checkpoints, monitor progress against milestones and KPIs, flag risks early, and ensure scope integrity throughout deployment
• Run technical governance checkpoints — architecture reviews, integration testing milestones, data quality gates, and deployment readiness assessments
• Manage enterprise-vendor dynamics during the build phase — ensure enterprises provide what vendors need (data, access, decisions) and vendors deliver what they committed to (timeline, quality, business fit
• Own impact validation — ensuring deployed solutions deliver measurable business results, not just working software
• Flag technical risks early and escalate where vendor delivery or enterprise readiness falls short

4. Tech Ecosystem Partnerships

• Support partnerships with major cloud and AI platform providers (e.g., AWS, Azure, GCP) — coordinating on technical enablement, reference architectures, and platform-level support for the programme
• Evaluate and advise on how market offerings can be leveraged within the programme

You Bring (Experience & Qualifications)

Must-Haves

• 8–12 years of experience with a strong technical foundation — solution architecture, enterprise architecture, data architecture, or AI/ML engineering — combined with consulting or advisory experience
• Demonstrated ability to design AI/digital solution architectures end-to-end: from business problem through data requirements, integration design, cloud deployment, and operational handover
• Working depth in at least one major cloud ecosystem (AWS, Azure, or GCP) and familiarity with AI/ML platform services, API integration patterns, and data pipeline design
• Hands-on experience building, deploying, or technically governing AI solutions (e.g., predictive analytics, NLP/LLM applications, process automation, conversational AI, agentic workflows)
• Experience working with SMEs or mid-market companies — understanding the constraints of limited data estates, lean IT teams, and pragmatic deployment requirements
• Credible with both audiences: able to explain technical architecture to a non-technical SME owner AND technically interrogate a vendor engineer's solution design
• Comfortable operating with high autonomy in a fast-moving, ambiguous programme environment

Nice-to-Haves

• Prior experience working with Singapore government-linked digital initiatives
• Familiarity with the Singapore SME landscape, particularly in sectors such as manufacturing, logistics, retail, F&B, or professional services
• Hands-on experience building or deploying AI solutions (e.g., customer service agents, document processing, conversational analytics)
• Comfort working in a high-autonomy, AI-augmented operating model (we use Claude and Claude Code extensively to build programme assets)

Why This Role

• Shape a first-of-its-kind national AI acceleration programme for Singapore SMEs
• Work directly with BCG partners and senior leadership
• Engage with major tech giants at a strategic level
• Flexible commitment model suited to independent consultants or portfolio professionals